Characterization of equilibrative nucleoside transport of the pancreatic cancer cell line: Panc-1
Bibliographic record
Abstract
Objectives: Gemcitabine is a nucleoside analog drug used as a first-line chemotherapeutic agent against pancreatic cancer.The characterization of cell lines is crucial for understanding drug resistance development in pancreatic cancer.In this study, we aimed to determine the nucleoside transport properties of Panc-1 cells, one of the commonly used pancreatic adenocarcinoma cell lines. Materials and Methods:To assess the presence of Equilibrative nucleoside transporter 1 (ENT1) in Panc-1 cells, we performed immunofluorescence staining, western blot analysis, and S-(4-nitrobenzyl)-6-thioinosine (NBTI) binding assays.We also conducted standard uptake assays to measure the sodium-independent uptake of [3H]-labeled chloroadenosine, hypoxanthine, and uridine.Additionally, we determined the half-maximal inhibitory concentration (IC50) of gemcitabine.Statistical analyses were performed using GraphPad Prism version 8.0 for Windows.Results: Sodium-independent uptake of [3H]-labeled chloroadenosine, hypoxanthine, and uridine was measured using standard uptake assays, and the transport rates were determined as 111.1 ± 3.4 pmol/mg protein/10 secs, 62.5 ± 4.8 pmol/mg protein/10 secs, and 101.3 ± 2.5 pmol/mg protein/10 secs, respectively.Furthermore, the presence of ENT1 protein was confirmed using S-(4-nitrobenzyl)-6-thioinosine (NBTI) binding assays (Bmax 1.52 ± 0.1 pmol/mg protein; Kd 0.42 ± 0.1 nM).Immunofluorescence assays and western blot analysis also showed the presence of ENT-1 in Panc-1 cells.The determined IC50 of gemcitabine in Panc-1 cells was 2 μM, indicating moderate sensitivity.Conclusion: These results suggest that Panc-1 is a suitable preclinical cellular model for studying nucleoside analog drug transport properties and potential therapies in pancreatic cancer and pharmaceutical research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".